A comparison of realized beta forecasting methods

This paper provides an overview of the methods of modelling realized betas and compares their forecasting performance. We divide the methods into two groups – the reduced-form group and the realized-covariance-matrix-based group. We show that the first group dominates the second in forecasting betas. This finding speaks to the principle of parsimony – a complicated model does not necessarily provide better performance than a simple one. Furthermore, we find that incorporating the long-memory feature can improve short-term beta (e.g.one-day-ahead) forecasts.